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EniasCailliau / install_docker.sh
Last active August 21, 2018 12:23
Install docker on ubuntu Amazon EC2 Deep Learning Base AMI (Ubuntu)
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable"
sudo apt-get update
apt-cache policy docker-ce
sudo apt-get install -y docker-ce
sudo systemctl status docker
sudo groupadd docker
sudo usermod -aG docker $USER
@EniasCailliau
EniasCailliau / install_nvidia_docker.sh
Last active August 28, 2018 15:43
Install nvidia-docker2 on ubuntu Amazon EC2 Deep Learning Base AMI (Ubuntu)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update
sudo apt-get install nvidia-docker2
@EniasCailliau
EniasCailliau / ssh_aws_instance.sh
Created August 21, 2018 12:48
ssh -i certificate.pem ubuntu@<Public DNS (IPv4)>
@EniasCailliau
EniasCailliau / activate_nvidia_docker.sh
Created August 21, 2018 12:55
Activate nvidia-docker2 as the default Docker runtime
cat <<"EOF" > /etc/docker/daemon.json
{
"default-runtime": "nvidia",
"runtimes": {
"nvidia": {
"path": "/usr/bin/nvidia-container-runtime",
"runtimeArgs": []
}
}
}
@EniasCailliau
EniasCailliau / test_nvidia_docker.sh
Created August 21, 2018 12:57
docker run --runtime=nvidia --rm nvidia/cuda nvidia-smi
@EniasCailliau
EniasCailliau / boot_gpu_docker
Last active August 21, 2018 13:32
script to boot docker with gpu support
docker run -it -p 8888:8888 -p 6006:6006 tensorflow/tensorflow:latest-gpu
@EniasCailliau
EniasCailliau / gist:988e121eda1be7b1d6300fd6e1beeda0
Created August 23, 2018 10:14
tf_sagemaker_version_check
import tensorflow as tf
print(f'TF Version: {tf.__version__}')
@EniasCailliau
EniasCailliau / tf_sagemaker_dataset_to_tfrecords.py
Created August 23, 2018 10:18
"""
Converts MNIST data to TFRecords file format
"""
import os
def _int64_feature(value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
def _bytes_feature(value):
@EniasCailliau
EniasCailliau / tf_sagemaker_load_mnist_fashion.py
Created August 23, 2018 10:32
fashion_mnist = tf.keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
# Shapes of training, test and validation set
print("Fashion MNIST:")
print("Training set (images) shape: {shape}".format(shape=train_images.shape))
print("Training set (labels) shape: {shape}".format(shape=train_labels.shape))
print("Test set (images) shape: {shape}".format(shape=test_images.shape))
print("Test set (labels) shape: {shape}".format(shape=test_labels.shape))
@EniasCailliau
EniasCailliau / tf_sagemaker_visualize_mnist_fashion.py
Last active August 23, 2018 10:36
labels_lookup = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress',
'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot'
]
plt.figure(figsize=(10,10))
for i in range(25):
plt.subplot(5,5,i+1)
plt.xticks([])
plt.yticks([])
plt.grid(False)